Abstract Alzheimer?s disease (AD) is a triple health threat ? with soaring prevalence, enormous costs and lack of effective treatment. However, efforts in drug discovery and repurposing for the treatment of AD have had limited success. The failure is largely attributed to the adoption of a reductionist model of ?one-drug-one-gene- one-disease?. As AD is a multi-facet complex disease, a new treatment approach is urgently needed to simultaneously target multiple pathological processes responsible for the onset and progress of AD, some of which are also common to other diseases that cause dementia. In this application, we will develop an innovative translational bioinformatics approach to addressing challenges in AD drug discovery. Our approach is based on a new paradigm of systems pharmacology, which focuses on defining multiple targets to a single drug or a drug combination, and studying the effect of the drug(s) on perturbing disease-causing networks. Over the last ten years, we have developed a novel structural systems pharmacology (SSP) platform that can predict genome-wide high-resolution protein-chemical interactions and correlate molecular interactions with phenotype responses. The SSP platform synergistically combines novel methods from machine learning, bioinformatics, biophysics, and systems biology. We have successfully applied the SSP platform to drug repurposing, polypharmacology, side effect prediction, precision medicine, and Genome-Wide Association Studies. Building on our successful proof-of-concept studies, and in close collaborations with experimental laboratories, we will develop, and rigorously test a novel SSP approach to AD drug repurposing and polypharmacology. Firstly, we will develop a multi-layered drug-gene-pathway-disease-side effect network model (MULAN) that links FDA-approved drugs with dementia and side effects through protein-chemical interactions, gene-disease associations, chemical-disease associations, and dementia-associated biological pathways through integrating multiple omics data. Secondly, we will improve and apply our proven successful SSP platform, which can accurately infer novel relations from sparse and noisy MULAN, to identify safe FDA- approved drugs that can be repurposed for AD treatment. Finally, we will experimentally test FDA-approved drugs identified for their binding activity of drug targets and anti-AD potency in cell and animal models. The successful completion of this project will provide an integrated computational modeling framework for AD drug repurposing and polypharmacology as well as identify novel targeted anti-AD therapeutics toward pre-clinical trials.